Governing Green-Line Transport for Sustainable and Resilient Cities: Traffic Modeling and Urban Mobility Analysis

The transition toward sustainable and resilient cities requires urban transport systems that combine efficient mobility management with environmental responsibility, digital innovation, and adaptive governance. This study investigates the concept of Green-Line Transport as an integrated framework for improving urban mobility through traffic modeling, data-driven analysis, and smart transportation technologies, using Petroșani, Romania, as a case study. The empirical component of this study combines document analysis, field observations, video-based traffic measurements, and calibrated transport modeling to assess current mobility conditions and alternative transport scenarios. Building on these empirical results, a conceptual Green-Line Transport framework is proposed in which real-time mobility data, Internet of Things (IoT) technologies, Intelligent Transportation Systems (ITSs), and machine-learning-based analytics represent prospective digital components for future implementation rather than technologies empirically deployed or evaluated in the present case study. This study further proposes a data-supported governance framework through which traffic-model outputs and mobility indicators can inform mobility-policy coordination, multimodal integration, infrastructure prioritization, environmental objectives, and resilience-oriented decision making. Governance is, therefore, addressed as a conceptual and decision-support dimension of the proposed framework rather than as an independently investigated institutional process. The analysis considers traffic flows, mobility patterns, user behavior, accessibility, and the interaction between conventional and sustainable modes of transport within the urban system. A conceptual Green-Line Transport framework is proposed as a closed-loop mobility-governance architecture that extends existing SUMP, ITS, smart-mobility, and data-driven approaches by linking strategic mobility objectives with calibrated traffic modeling, real-time monitoring, predictive analytics, resilience assessment, adaptive decision making, intervention, and continuous performance reassessment. The framework also supports the identification of mobility vulnerabilities and priority interventions for local authorities and urban planners. This study demonstrates the potential of integrating digital technologies and advanced traffic analytics within a proposed governance-oriented decision-support framework for more efficient, sustainable, and resilient urban transportation. The proposed approach provides a transferable methodological basis for medium-sized cities seeking to implement smart mobility strategies and advance the transition toward sustainable and resilient urban development.